Syllabus

AREC 261 – Fall 20261

Course information

  • Instructor: Peter Slade, peter.slade@usask.ca, Room 3E78 (Agriculture Bldg)

  • Office hours: I have no set office hours. As I will explain, the best time to ask about course material is during our lab sessions. Otherwise, I’m happy to set up a time to meet in my office. You can also drop by anytime to see if I am in – there is a 60% chance I will be there during normal working hours and a 15% chance I will be there during abnormal hours. Please note that I can be forgetful; if I agree to do something for you during an in-person meeting, please send me a brief email summarizing what we had agreed to in our conversation. Email is great for minor or administrative issues, but isn’t ideal for lengthier questions about course content. I normally respond within one business day. If you have not heard back by then, please send me a reminder.

  • Teaching assistant: Nazila Mohtashami, nam909@usask.ca.

  • Course materials: https://AgDataAnalytics.com

Introduction

Welcome to Agricultural Data Analysis I! This is part of a two-class sequence (alongside AREC 262) in agricultural data analysis. At the end of these classes you should be comfortable doing basic analysis on agricultural data sets in both Excel and R. In this class that means loading in data, filtering and organizing it, summarizing it, graphing and visualizing it, working with spatial and field-level agricultural data, an introduction to models and correlation, and communicating what you have learned. AREC 262 then turns to data modelling – starting with an understanding of probability and building up to hypothesis tests and forecasting.

It is my hope that you will build on these skills in third- and fourth-year classes, and when you graduate you will be prepared for data-intensive jobs in agribusiness or farm management. More basically, you should have a data literacy that ensures you are no longer fooled by randomness.

Course organization

The organization of this class is unique. It is very important we are all on the same page right from the outset. Let me explain the nuts and bolts of how the class works, and then I will try to convince you why this is an upgrade over the standard lecture and lab system.

There are no lectures or standard classes. All the material is online. The scheduled class times (Mon, Wed, Fri 11:30am - 12:20pm) will be used only for taking tests (other than our first class together). The lab times (Mon and Tue 2:30pm-4:50pm) will be used for unstructured instruction – come with questions or ask me to do practice questions (attendance is voluntary).

Here is how it works:

  1. The class is divided into twelve modules, reflecting the thirteen weeks of the term (less the first week, which serves more as an orientation).
  2. The instructional material for each module is online. This material is a textbook with embedded videos, and links to external resources.
  3. There is an in-class test for each module, with the exception of Modules 5 and 12, which have assignments instead of tests. Tests can be taken during our Monday, Wednesday, and Friday class time.
  4. There is a deadline for taking the test for each module, but you can take the test during any class before the deadline. In theory, you could take a test every day for the first four weeks of class and be done. Or you can take a test every week and complete the course over the 13 weeks of the semester.
  5. To pass a module, you must earn a raw score of at least 80% on its test or assignment. Tests can be retaken and assignments can be revised and resubmitted as needed (up to three attempts per week), though the recorded module mark goes down with each attempt (see grading below).
  6. Final exam. If you have finished all modules, you can take the final exam during any Monday or Tuesday lab session during the term. Otherwise, you can take the final during the final exam period.

Evaluations

  • Module tests and assignments (12 @ 6.25% each): 75%
  • Final exam: 25%

Tests

Procedure for taking a test

Here is the procedure for taking a test.

During a test, you may use only Canvas, the supplied dataset, and the software specified for that module. You may not use the course website or textbook, notes, previously saved scripts or workbooks, internet searches, messaging applications, or AI assistance. AI features embedded in otherwise permitted software are also not permitted during tests.

  • At least an hour before the test, “Schedule an appointment” in the test for that day.
  • Bring your (charged) laptop to the test.
  • Your test will be accessible at 11:30am.
  • When you get to the room:
  1. Close all applications other than your web browser (used to access the test) and the software the module uses (Excel for the Excel modules; Positron and R for the R modules).
  2. Log into Zoom and join the breakout room with your name on it.
  3. In Zoom, share your screen (not an application window, but your whole screen) and record your session.
  4. Navigate to Canvas and access your test.
  5. Download the dataset that you need.
  6. Complete the test in the module’s software.
  7. Upload the completed workbook or script to Canvas (while still logged into Zoom).
  8. Leave Zoom, and upload the screen recording to Canvas.
  9. You’re done!

Grading

  1. If your raw score on your first attempt is 80% or above, that score will be your recorded module mark.
  2. If your raw score is below 80%, you must try again. If you pass on your second attempt, your recorded module mark will be your raw score on that attempt multiplied by 0.8. For example, a raw score of 100% on a second attempt produces a recorded module mark of 80%.
  3. More generally, if you earn a raw score of at least 80% on your \(N\)th attempt, your recorded module mark will be \(X \times 0.8^{N-1}\), where \(X\) is your raw score on the successful attempt.

These same mastery and attempt rules apply to the assignments in Modules 5 and 12. For an assignment, a revised submission counts as the next attempt.

Tests will usually be marked within 24 hours.

If you do not take a test by the deadline, it will be treated as an attempt with a mark below 80%. You are then required to take the test in the next class, and that test is marked as a retake. For example, if the deadline for taking a test is the Wednesday of week 5, you must take the test by that date. If you do not pass, you must re-take in the next class, and then the class after that. For this reason, I encourage you to stay ahead of the test deadlines.

Assessment deadlines

The deadline for each module’s test or assignment is shown in the course module schedule below.

Final exam

The final exam is a 50-minute, in-person, computer-based assessment. It will have questions drawn from the test banks of all the modules in the course and will likely contain about 10 questions. The same closed-resource rules that apply to module tests apply to the final exam. You can take the final exam during any Monday or Tuesday lab time – I will set up a scheduling option in Canvas for this. Otherwise, you can take the final during the final exam period, which runs from December 8 to December 23.

There is no midterm exam.

Criteria to pass

To pass the course, you must earn a raw score of at least 80% on the assessment for every module. Your recorded module mark may be lower than 80% after the attempt penalty is applied.

Participation

There is no requirement for participation. I strongly encourage you to participate in lab sessions.

Course modules

Course materials for every module are available at https://AgDataAnalytics.com.

  1. Introduction to Excel – Test; deadline: Fri, Sep 11
  2. Introduction to R – Test; deadline: Fri, Sep 18
  3. Getting and Combining Data – Test; deadline: Fri, Sep 25
  4. Graphing – Test; deadline: Fri, Oct 2
  5. Using Artificial Intelligence – Assignment; deadline: Fri, Oct 9
  6. Spatial Data and Mapping – Test; deadline: Fri, Oct 16
  7. Field-Level Data – Test; deadline: Fri, Oct 23
  8. External Spatial Data – Test; deadline: Fri, Oct 30
  9. Thinking About Modelling Data – Test; deadline: Fri, Nov 6
  10. Correlation and Simple Linear Models – Test; deadline: Fri, Nov 20
  11. Advanced Visualization and Interactive Displays – Test; deadline: Fri, Nov 27
  12. Communicating and Delivering Results – Assignment; deadline: Fri, Dec 4

Why this format?

First, software skills are learned by doing. Watching me filter a spreadsheet in a lecture is a poor substitute for filtering one yourself, getting stuck, and figuring out why. Plus, you have the unpleasant task of watching me try to lecture. The online material is built for doing: every example in the textbook can be opened and run, and the practice questions are the same questions you will be tested on. Class time is then freed up for the part of teaching that will provide more value – answering your questions either one-on-one or in a smaller group.

Second, these skills stack. If you half-learn how to read data into R, everything built on top of it wobbles. In past classes, students would do poorly on early modules and be lost for the whole course. That is why each test requires 80% to pass and why you can retake test.

Third, students move at different speeds and may have a different level of comfort with different material. Some students might already know Excel and zip though module 1, but need more time on module 5.

The honest trade-off is that this format asks more self-discipline of you than a typical course. The deadlines and the retake penalty are there as guardrails to help, but you are the one in control of your learning. I am here to help in anyway I can – whether it is giving you detailed guidance or just cheering you on.

Some questions / concerns you might have

  1. Can I complete all the course content as soon as I want? Yes! The only thing limiting your progress through the course is that you can only take the module tests during our class times. In theory, you could take a test in each class, starting the first Friday. And then you would be ready for the final exam in four weeks.

  2. Peter, this seems like a way for you to avoid doing your job, which is teaching us data analysis. Fair comment! One note I would make is that the lab time is there for me to teach you in smaller groups. I think I am a more effective teacher in small groups than I am in a lecture (students who have been forced to listen to me in the past can attest to this!). I truly believe that this format will enhance your learning.

  3. Should (or will) all of our classes move to the same format? Probably not! This class is very quantitative and has a focus on software. It is easier to deliver “hard” skills through this type of format. This format would work less well for classes like Agricultural Policy, which have a greater focus on discussion, brainstorming and judgment.

Artificial intelligence

I am a heavy user of AI. Be forewarned, that some colleagues find me a bit extreme in my optimistic view on the power of AI. You are strongly encouraged to use AI as a learning tool. I have created a class GPT that you can chat with about course material. In Modules 5 and 12, the assessment is an assignment for which you are asked to use generative AI. You must acknowledge the tools that you used and how you used them.

However, it is also important that you learn the course material enough to guide AI in your data analysis. For this reason, use of AI on in-class tests is not permitted and will be treated as academic misconduct.

Academic misconduct

See below for the USask policy on academic misconduct. As all assessments are in-class, academic misconduct in this class is fairly black and white. Any outside tool used to help you on your test (other websites, generative AI, previously saved files) is considered academic misconduct. Similarly, using any outside material is also considered academic misconduct. I have zero tolerance for cheating: academic misconduct will be reported to the Academic Administrator with a recommendation that the student receive a grade of zero in the class.

Learning outcomes

By the end of this course, you should be able to:

  1. Organize, summarize and describe agricultural datasets in Excel, using descriptive statistics, conditional functions, lookups, and PivotTables.
  2. Write well-commented, reproducible R scripts that read, filter, and summarize data.
  3. Import data from files, the web, and R packages, and document where the data came from.
  4. Find and fix common data-quality problems: duplicates, inconsistent categories, mixed units, and missing-value codes.
  5. Merge related tables and check the result.
  6. Work with spatial and field-level agricultural data, including making simple maps.
  7. Use AI coding tools productively, and verify their output.
  8. Interpret simple models and correlations, and explain why correlation is not causation.
  9. Communicate results in clear tables, charts, and short written reports.

University policies and student supports

Academic Courses Policy

The USask Academic Courses Policy contains the requirements for course delivery, examinations, and other forms of student assessment. You can view the policy at https://policies.usask.ca/policies/academic-affairs/academic-courses.php.

You can also read information on the following policies and procedures:

Academic Integrity

The University of Saskatchewan is committed to the highest standards of academic integrity (https://academic-integrity.usask.ca/).

Students are urged to read the Regulations on Academic Misconduct and to avoid any behaviours that could potentially result in suspicions of cheating, plagiarism, misrepresentation of facts and/or participation in an offence. Students are encouraged to ask their instructors for clarification on academic integrity requirements.

Access and Equity Services (AES)

Access and Equity Services (AES) is available to provide support to students who require accommodations due to disability, family status, and religious observances.

Students who have disabilities (learning, medical, physical, or mental health) are strongly encouraged to register with Access and Equity Services (AES) if they have not already done so. Students who suspect they may have disabilities should contact AES for advice and referrals at any time. Those students who are registered with AES with mental health disabilities and who anticipate that they may have responses to certain course materials or topics, should discuss course content with their instructors prior to course add / drop dates.

Students who require accommodations for pregnancy or substantial parental/family duties should contact AES to discuss their situations and potentially register with that office.

Students who require accommodations due to religious practices that prohibit the writing of exams on religious holidays should contact AES to self-declare and determine which accommodations are appropriate. In general, students who are unable to write an exam due to a religious conflict do not register with AES but instead submit an exam conflict form through their PAWS account to arrange accommodations.

Any student registered with AES, as well as those who require accommodations on religious grounds, may request alternative arrangements for mid-term and final examinations by submitting a request to AES by the stated deadlines. Instructors shall provide the examinations for students who are being accommodated by the deadlines established by AES.

For more information or advice, visit https://students.usask.ca/health/centres/access-equity-services.php, or contact AES at 306-966-7273 (Voice/TTY 1-306-966-7276) or email .

Student supports

Academic Help – University Library. Visit the University Library and Learning Hub to find supports for undergraduate and graduate students with first-year experience, study skills, learning strategies, research, writing, math and statistics. Students can attend workshops, access online resources and research guides, book 1-1 appointments or hire a subject tutor through the USask Tutoring Network. Connect with library staff through the AskUs chat service or visit various library locations on campus. Enrolled in an online course? Explore the Online Learning Readiness Tutorial.

Teaching, Learning and Student Experience. Teaching, Learning and Student Experience (TLSE) provides developmental and support services and programs to students and the university community. For more information, see the students’ website https://students.usask.ca.

Financial Support. Any student who faces unexpected challenges securing their food or housing and believes this may affect their performance in the course is urged to contact Student Central.

Gordon Oakes Red Bear Student Centre. The Gordon Oakes Red Bear Student Centre is dedicated to supporting Indigenous students’ academic and personal success. The Centre offers personal, social, cultural and some academic supports to Métis, First Nations, and Inuit students. It is an intercultural gathering space that brings Indigenous and non-Indigenous students together to learn from, with and about one another in a respectful, inclusive, and safe environment. Visit https://students.usask.ca/indigenous/gorbsc.php for more information.

International Student and Study Abroad Centre. The International Student and Study Abroad Centre (ISSAC) supports student success and facilitates international education experiences at USask and abroad. ISSAC is here to assist all international undergraduate, graduate, exchange, and English as a Second Language students in their transition to the University of Saskatchewan and to life in Canada. ISSAC offers advising and support on matters that affect international students and their families and on matters related to studying abroad as University of Saskatchewan students. Visit https://students.usask.ca/international/issac.php for more information.

  1. I acknowledge use of AI tools (Claude Fable and ChatGPT Sol) in preparing the course content, including brainstorming, editing, and content generation.↩︎